Make: Volume 52

If

you’ve been living on Earth this year,

you’ve probably heard someone

mention self-driving cars. If you

were tuned in, you might have heard the

$70,000 price tag for just one of the early

lidar units used in Google’s driverless cars.

The devices are a bit outside the typical

price point for a family auto let alone a

hobbyist’s project.

Lidar is at once an acronym for Light

Detection and Ranging and a portmanteau

of “light radar.” Its name is fairly descriptive:

The unit bounces laser light off of objects,

and senses the return of that light to

measure the distance of said object. This is

key to object detection in a self-driving car.

But forget cars for a moment. What

about golf carts?

Alex Rodrigues, Michael Skupien, and

Brandon Moak built a self-driving golf cart

as part of a mechatronics engineering

program at the University of Waterloo in

Ontario, Canada. The sensor that made

it possible was a new-ish, $8,000 lidar

unit from Velodyne called the VLP-16,

affectionately known as the Puck. After a

$35,000 grant, Rodrigues, Skupien, and

Moak dropped out of Waterloo to move to

California and attend YCombinator. They

founded Varden Labs, which just finished

a round of fundraising, and has done pilot

demos for automated shuttles, including

bringing VIPs to a Sacramento Kings game.

That’s not to say lidar makes automated

driving simple, but if you can take some of

the challenges out, reasoned Rodrigues,

you could make it a lot easier to solve. “We

intentionally chose to put constraints on

the problem,” he says. “We said building

a fully self-driving car that can work in

all conditions, in all places, at all times, is

really hard. But making something that can

be useful for transporting people doesn’t

have to be that hard. So we specifically

are targeting shuttle service on private

campuses.” That means they could slow it

down, place it in a controlled environment

like a university or retirement community,

and keep it on private property to cut down

on the regulations they have to deal with.

A CHANGING LANDSCAPE

Varden Labs is just one of a number of

groups tackling self-driving golf carts

as people movers, and there’s still more

working in other segments of lidar

computer vision and control. And not all

are targeting big, expensive vehicles; the

technology is rapidly becoming more

accessible to makers — who are in turn

making it cheaper and more accessible for

the masses.

So forget self-driving golf carts. Reduce

the problem even more. What about lawn

mowers?

“We are trying to make it better than the

average mower,” says Alexander Grau, one

of five members of the Ardumower project,

an Arduino-powered DIY automated cutter.

“If you want average quality or average

features, then you just can buy a normal

mower, commercial mower. If you want

something special, then you have to build

yourself a mower.”

Basically a Roomba for your lawn, right?

If only it were that simple. Roombas and

other robotic vacuums (some of which use

lidar) have a couple of advantages: They

(mostly) don’t operate in direct sunlight;

they never have to go up- or downhill;

and their environment doesn’t change

much. Take it outside, put a blade on it,

and you have to contend with many other

confounding factors.

“If you have flat ground, just a room for

example, indoor it works nice,” says Grau.

“But outdoor it’s too difficult, because

the ground is not flat. So sometimes you

get data, signal from the ground, and

the processing software … thinks it’s an

obstacle, but it’s just the ground.”

Grau would know. He’s a computer

scientist who is currently tackling all

these issues, and more, in pursuit of the

Ardumower. You can buy an automated lawn

mower, points out Grau, but it relies on a

cable strung around the perimeter to stay in

bounds. Lidar seemed a better option.

In principle, lidar isn’t very difficult. But

its basic element only gives you one data

point: You know that something is a certain

distance away, but not how wide or tall it

is, if it’s moving, how it’s oriented, or really

anything else. A person appears the same

as a wall appears the same as a chair, if the

chair is even tall enough to be struck by the

laser. To make lidar useful, you need to take

many measurements over a period of time.

And there’s more than one way to do that.

Most simply, affix a unit on a spinning

makezine.com 17

makezine.com/52

CAPTURING

ROUTES

A multi-laser lidar

unit like those from

Velodyne can quickly

capture a three-

dimensional model

of a locale when set

in a static position.

The more advanced

the unit, the more

lasers it contains,

which in turn collect a

higher density of data,

which allows for fairly

detailed 3D images of

their location.

Once you start

moving a lidar

unit, however, the

data it is capturing

begins to overlap,

making recordings

impossible to parse.

Sophisticated systems

overcome this by

adding in positional

awareness like GPS.

Some of the mapping

vehicles from big tech

companies couple

GPS with lidar units

to track the exact

point where each

moment of lidar data

is captured, “painting”

their route with

lasers. Combined

with the location

data, this can be used

to create a nearly

infinite 3D point cloud,

valuable for various

applications such as

providing detailed

street information to

self-driving cars.

M52_016-9_20_LiDAR_F1.indd 17 6/12/16 5:59 PM